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The expert experience in adopting E-S-QUAL scale

机译：采用E-S-QUAL规模的专家经验

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SERVQUAL has been in use for measurement of service quality for many years. However, some difficulties in its use have been reported such as using different scores between expectations and perceptions, the ambiguity in the definition of consumer expectations, the instability of the scale over time and the dimensionality of the instrument. Thus researchers were looking for alternative measures for the measurement of service quality. It is very important due to tough competition, that online providers would like to monitor their service quality continuously so that they may take measures in case of any erosion in the service quality level. There have been many discussions that took place on this issue as to how to measure online service quality flawlessly. Finally after rigorous studies Parasuram, Zeithaml and Malhotra (Ref. 1) proposed E-S-QUAL for this purpose. This has received considerable attention among users as the scale shows good psychometric properties. However, there has been many discussions among researchers about the usefulness of this instrument to evaluate any e-service quality. This study reviews some of the studies in which E- S-SQUAL was used. One drawback that was found is the dimensional structure of this instrument is unstable. There were some differences in the sample characteristics and data analysis procedures among the studies where E-S-QUAL was used. This article explores the reasons for instability such as sampling errors, methodology used and the data analysis methods employed and whether the scale is generic or product specific.
机译：ServQual已用于测量服务质量多年。然而，已经报告了一些困难，例如在期望和感知之间使用不同的分数，消费者期望定义的模糊性，随着时间的推移和仪器的维度的不稳定。因此，研究人员正在寻找服务质量测量的替代措施。由于艰难的竞争，这是非常重要的，即在线提供商希望不断监控他们的服务质量，以便在服务质量水平的任何侵蚀的情况下，他们可能会采取措施。对于如何完美地衡量在线服务质量，因此发生了许多讨论。最后经过严格的研究，Parasuram，Zeithaml和Malhotra（Ref.1）提出了E-S-UST的目的。这在用户中受到了相当大的关注，因为规模显示出良好的心理测量属性。但是，研究人员对本文的有用性进行了许多讨论，以评估任何电子服务质量。本研究审查了一些研究中使用的研究。发现的一个缺点是该仪器的尺寸结构是不稳定的。使用E-S-USL的研究中的样本特征和数据分析程序存在一些差异。本文探讨了不稳定性的原因，例如采样错误，使用的方法和使用的数据分析方法以及比例是通用的还是产品特定的。

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